Spectral Subtraction for Speech Denoising

In modern society, we face various types of noise interference, particularly in the field of speech communication. To address this challenge, spectral subtraction has emerged as an effective method widely applied for speech denoising. This algorithm works by estimating the noise spectrum during non-speech segments and subtracting it from the noisy signal's spectrum in the frequency domain. The implementation typically involves framing the audio signal, applying Fast Fourier Transform (FFT) to convert to frequency domain, estimating noise parameters, and performing spectral subtraction before reconstructing the signal through inverse FFT.

Using spectral subtraction for speech denoising effectively removes additive noise superimposed on speech signals, thereby ensuring the quality of speech communication. Key functions in the implementation include noise estimation during silent intervals, magnitude spectrum subtraction with over-subtraction factors to prevent musical noise, and phase preservation during signal reconstruction. The method offers practical utility and can be applied to various scenarios such as meeting recordings, telephone communications, speech recognition systems, and other audio processing applications. Therefore, spectral subtraction for speech denoising represents a highly valuable technology worthy of widespread adoption.